Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

Phillip Harder,Michael Schirmer,John Pomeroy,Warren Helgason

doi:10.5194/tc-10-2559-2016

Abstract

Abstract. Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover.

Highlights

Accumulation, redistribution, sublimation and melt of seasonal or perennial snow cover are defining features of cold region environments
The introduction of functional unmanned aerial vehicle (UAV) to the scientific community requires a critical assessment of what can reasonably be expected from these devices over seasonal snow cover
Field campaigns assessed the accuracy of the Ebee Real Time Kinematic (RTK) system over flat prairie and complex terrain alpine sites subject to wind redistribution and spatially variable ablation associated with varying surface vegetation and terrain characteristics

Summary

Introduction

Accumulation, redistribution, sublimation and melt of seasonal or perennial snow cover are defining features of cold region environments. Snow is generally quantified in terms of its snow water equivalent (SWE) through measurements of its depth and density. Since density varies less than depth (López-Moreno et al, 2013; Shook and Gray, 1996) much of the spatial variability of SWE can be described by the spatial variability of snow depth. The ability to measure snow depth and its spatial distribution is crucial to assess and predict how the snow water resource responds to meteorological variability and landscape heterogeneity. Observation and prediction of the spatial distribution of snow depth is even more relevant with the anticipated and observed changes occurring due to a changing climate and land use (Dumanski et al, 2015; Harder et al, 2015; Milly et al, 2008; Mote et al, 2005; Stewart et al, 2004)

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